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hal.structure.identifieremlyon business school [EM]
dc.contributor.authorBrigui-Chtioui, Imène
dc.contributor.authorCaillou, Philippe
HAL ID: 5485
hal.structure.identifierLaboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
dc.contributor.authorPinson, Suzanne
dc.date.accessioned2023-01-09T13:54:27Z
dc.date.available2023-01-09T13:54:27Z
dc.date.issued2022
dc.identifier.urihttps://basepub.dauphine.psl.eu/handle/123456789/23672
dc.language.isoenen
dc.subjectDecision modelsen
dc.subjectAutomated auctionsen
dc.subjectMultiagent systemsen
dc.subjectBidding strategiesen
dc.subject.ddc006.3en
dc.titleAdaptative Strategies for Multicriteria Auctions : an Empirical Studyen
dc.typeCommunication / Conférence
dc.description.abstractenBook coverInternational Conference on Information Systems and Management ScienceISMS 2021: Information Systems and Management Science pp 174–184Cite asAdaptive Strategies for Multicriteria Auctions: An Empirical StudyImène Brigui, Philippe Caillou & Suzanne Pinson Conference paperFirst Online: 29 November 202247 AccessesPart of the Lecture Notes in Networks and Systems book series (LNNS,volume 521)AbstractIn this paper, we propose bidding strategies for conducting automated reverse auctions based on a non-compensatory multicriteria model. We conduct an empirical multiagent study in order to appreciate the relevance of the proposed strategies. In this type of auction and in order to ensure ascending process evolution, the design of such automated systems often uses a fixed bid increment representing the minimum amount by which a bidder must improve on the current best bid. This article suggests adjusting the bid increment as the auction process goes on. To this end, we propose buyer counterproposal which ensures an acceptable solution at any given time. To this end, we refer to the auction context based on the number of remaining suppliers or the remaining time at each process step. We also present and demonstrate some interesting properties of the proposed algorithm. Finally, we provide an empirical study that compares a fixed-increment strategy to our proposed strategies on the basis of a variation of different auction settings.en
dc.identifier.citationpages174–184en
dc.relation.ispartoftitleInformation Systems and Management Scienceen
dc.relation.ispartofeditorLalit Garg, Dilip Singh Sisodia, Nishtha Kesswani, Joseph G Vella, Imene Brigui, Peter Xuereb, Sanjay Misra, Deepak Singh
dc.relation.ispartofpublnameSpringeren
dc.relation.ispartofpublcityBerlin Heidelbergen
dc.relation.ispartofdate2022-11
dc.relation.ispartofpages580en
dc.relation.ispartofurl10.1007/978-3-031-13150-9en
dc.subject.ddclabelIntelligence artificielleen
dc.relation.ispartofisbn978-3-031-13149-3en
dc.relation.conftitle4th International Conference on Information Systems and Management Science (ISMS) 2021en
dc.relation.confdate2021-12
dc.relation.confcityMsidaen
dc.relation.confcountryMaltaen
dc.relation.forthcomingnonen
dc.identifier.doi10.1007/978-3-031-13150-9_16en
dc.description.ssrncandidatenon
dc.description.halcandidatenonen
dc.description.readershiprechercheen
dc.description.audienceInternationalen
dc.relation.Isversionofjnlpeerreviewednonen
dc.date.updated2023-01-09T13:48:23Z
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